Question 7 2 pts Multiple regression is the process of using several independent variables to predict...
Multiple regression is the process of using several independent variables to predict a number of dependent variables. True O False
Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity among the independent variables is often a concern. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? Can you still achieve a high r for your regression equation if multicollinearity is present in your data? Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity...
Question 5 (1 point) The multiple regression model includes several dependent variables. True False Question 6 (1 point) Dummy variables for regression analysis can take on a value of either -1 or +1. True False Question 7 (1 point) The several criteria (maximax, maximin, equally likely, criterion of realism, minimax regret) used for decision making under uncertainty may lead to the choice of different alternatives. True False Question 8 (1 point)
11. Multiple regression analysis is used when one independent variable is used to predict values of two or more dependent variables. True or False 13. For a two-tailed null hypothesis, the test statistic Z=1.96. Therefore, the p-value is 0.05. True False
Question 6 1 pts The multiplication of two variables is used as a predictor if the two variables jointly affect the response. O True O False Question 7 1 pts Even if the P-value of the Ftest in a multiple regression model is nearly zero, it is possible that the R2 of the model is much less than one. O True False Question 8 1 pts In selecting independent variables for a regression model, neither the forward selection method nor...
Question 2 1 pts In an ANOVA test comparing several population means, if the alternative hypothesis is true, the F statistic tends to be close to zero. True False Question 3 1 pts If the two variables in a two-way table are not associated, the conditional distributions in the table are similar to each other. O True False Question 4 1 pts In a multiple regression model, if the P-value associated with the F test is less than the significance...
D Question 7 2 pts Only coefficients with a large standard error can be statistically significant. True False D Question 8 1 pts If you estimate a regression model and the R-square is 0.50, how much of the variation in the dependent variable is explained by the independent variables O 10% O 25% 50% О 100%
In multiple regression, the adjusted R2 controls for the number of dependent variables. True False
TRUE OR FALSE: We cannot avoid multicollinearity in a multiple regression as the independent variables are always correlated with each other to some extent? Perfect multicollinearity means independent variables are - perfectly correlated - positively correlated - highly correlated - not correlated Near multicollinearity means independent variables are - perfectly correlated - positively correlated - highly correlated - not correlated
QUESTION 1 The Simple Linear Regression is fit or constructed to predict a dependent variable. True False QUESTION 2 The Coefficient of Determination is used to explain in what percent (%) the independent variable is affecting the dependent variable. True False